Become the Generative AI Engineer
companies fight to hire.
Build, don't just call APIs. A 24-week live cohort built around real GenAI engineering. Neural nets in NumPy. GPT from scratch in PyTorch. RAG, agents, fine-tuning, and LLMOps on open-source. For engineers serious about shipping AI products.
Enroll in Spring Cohort
Takes only 2 minutes · Confirmed within 24 hours
⚡ Strictly Capped at 30 Seats to Ensure Individual Flagship Portfolio Project Review Quality
Open-Source Stack We Teach Native Autonomy:
Why Most AI Prototypes Never
Reach Production
The hard truth about calling basic wrapper APIs vs. building native infrastructure layers that scale without breaking budget, accuracy, or latency gates.
of GenAI prototypes never reach production. They hit architecture walls: hallucination, token evaluation drift, high latency, and zero observability metrics.
of wrapper engineers cannot fine-tune a 7B model, write custom hybrid retrieval hooks, or deploy programmatic evaluation guardrails in CI pipelines.
of engineers own the full AI stack — from loss gradient steps to LangGraph loops. They command premium tech salaries because hiring managers recognize real shipping capability.
We exist because every one of those production gaps is closed by building, not watching. 24 weeks. 16 capstones.
See The Full Stack System In Action
Watch a 90-second technical walkthrough showing how model components, vector storage layers, and orchestration graphs compose together.
Watch 90s Walkthrough
Click to play the technical overview
Five Pillars. One Shippable
Generative AI Engineer.
Built completely around end-to-end framework assembly. Fundamentals first, zero magic black boxes.
Fundamentals First
No framework wrappers before you grasp embedding matrix dimensions, vector mathematics, loss mechanics, and token sampling probabilities.
Open-Source Native
Master PyTorch, Hugging Face, LangGraph, vLLM, and Qdrant natively. No proprietary API wrapper crutches or vendor lock-in dependencies.
Capstone Driven
Every development phase closes with a functional portfolio element. Build a line-by-line decoder Transformer up to multi-agent engines.
Live Group Cohort
Two 90-minute live interactive sessions weekly with asynchronous engine lab debugging logs and structured pull-request reviews.
Production LLMOps
Most programs finish at a streamlit sandbox. We cover dedicated deployment runtimes, real-time logging evaluation gates, tracer hooks, and guardrails.
What You'll Actually Ship:
The Entire GenAI Lifecycle
Models, search architectures, and autonomous steps don't exist in siloes. You'll code every linking pathway across the platform flow.
Build & Fine-Tune
Transformers from first principles in PyTorch. QLoRA and DPO fine-tuning mechanics on Llama, Mistral, and Qwen blocks. Optimize model execution with vLLM runtimes, quantized matrices, and cached structures.
Retrieve & Ground
Embeddings, advanced custom segment indexing, hybrid sparse-dense indexes, structural query splits, reranking pipelines, and multi-modal search engines using ChromaDB, Qdrant, and pgvector clusters.
Compose & Ship
Multi-agent architecture logic with structural graph states via LangGraph. Wire production schema calls, state tracking history, validation gates, runtime evaluation systems, and live tracing instances.
Bridge The Divide Between
Tutorial Demos and Production Realities
Stop relying on quick code recipes. Master the tooling systems running actual enterprise instances.
No Hidden Wrapper Framework Crutches
You'll configure backpropagation matrices in raw NumPy, build custom neural layers, and assemble an entire GPT framework token step to see exactly how indices transform.
Comprehensive Infrastructure Testing
Setup production evaluation setups using automated scoring pipelines. Instrument execution tracks with detailed traces to analyze cost, prompt accuracy, and latency variables.
Open Parameter Tuning and Alignment
Format training sets for open model adaptation. Spin up fine-tuning targets to alter system behavior, introduce context skills, and clean structured outputs down to raw parameters.
Target Modern High-Leverage Positions
Generative AI Engineer
Architect complex system environments using custom semantic indexes, programmatic contextual hints, and open model stacks.
LLMOps Infrastructure Engineer
Deploy scalable model instances, instrument evaluation tracking dashboards, partition vector clusters, and manage caching runtimes.
Applied ML/AI Architect
Design multi-modal model architectures, implement fine-tuning runtimes, and construct parameter alignment datasets.
Founding AI Engineer
Translate initial functional ideas into resilient software prototypes by keeping framework assemblies clear and highly performant.
Generic Tutorials vs. Production Competence
Assembling native components beats copy-pasting API endpoints every single time.
YouTube Guides / Basic Wrappers
- No native matrix implementation from baseline code blocks
- Proprietary black-box wrappers used as primary tools
- Zero system evaluation scoring arrays or pipeline gates
- Omit system deployment patterns or observability trackers
- Fragmented scripts that fail outside sandbox environments
- Self-paced instruction without structural codebase feedback
ExaGuru GenAI Engineering Cohort
- Transformers and backprop engineered from scratch
- 100% open-source component stack native autonomy
- Production evaluation platforms using automated scoring runtimes
- Deep operational focus covering serving runtimes and trace trees
- 16 concrete milestone projects and one flagship pipeline
- Automated validation test setups with individual architecture feedback
Five Specialized Development Phases.
Complete Engineering Track.
24 Weeks · 10–12 Hours/Week · 16 Milestone Projects · 1 Flagship Autonomous Assistant Production Build
Foundations, Math & Environment Pipeline
- Python environmental compilation structures using uv, Conda, and container runtimes
- NumPy and Pandas deep execution vectors for array transformation workflows
- Linear algebra, multidimensional matrices, gradients, and calculation steps
- Probability fields, maximum likelihood metrics, and Bayesian assumptions
- Classical machine learning algorithms via scikit-learn to map system validation metrics
- Phase Capstone: End-to-end telemetry workflow execution using modern data arrays
Deep Learning Nodes to Transformer Blocks
- Multilayer Perceptron networks written from basic mathematical steps inside raw NumPy
- PyTorch programmatic operations: autograd hooks, precision loops, and compute setups
- Computer Vision feature extraction layers, adaptation setups, and interface testing
- Attention mechanisms and multi-headed query block transformations
- Phase Capstone: Building a line-by-line character decoder model engine
Generative Architectural Patterns
- Autoencoders, representation bounds, and state reparameterization rules
- Generative Adversarial Nets: boundary checks, stabilization steps, and translation hooks
- Diffusion pipelines: mathematical parameters, guidance features, and latent states
- Autoregressive sequences, text parsing rules, and structural scaling trends
- Phase Capstone: Assembling a latent generation workflow with custom input constraints
Open Models, Custom Adaptation & Vector Stores
- Open parameter space structures (Llama, Mistral, Qwen) vs closed API formats
- Prompt engineering metrics, execution hooks, and evaluation frameworks
- Fine-tuning runtimes: SFT scripts, PEFT formats, and parameter alignment routines
- Embedding pipelines, high-dimensional search indices, and retrieval matching
- Phase Capstone: Tuning a 7B model space and deploying a semantic search route
Advanced Retrieval, Graph Agents & Scale LLMOps
- Semantic processing systems, visual search indexes, and parsing evaluation criteria
- Autonomous state charts using LangGraph routines, tool logic, and memory tables
- Runtimes optimization targets, tracking architectures, and automated regression testing
- Multi-modal interfaces, sound transcription steps, and real-time streaming routes
- Flagship Capstone: Assembling a voice-driven multi-agent semantic platform instance
You Don't Read Papers. You Ship Pipelines.
Every phase completes with a concrete code lab validated via comprehensive suite tests. Build clean portfolio structures.
Live Cloud Accelerators. Automated Regressions.
Run complex tasks across automated test containers equipped with full GPU access allocations without hardware limits.
- Compute instances with scalable cloud access tiers included
- Automated validation test setups with line-by-line file inspections
- Production reference solutions for production structural reviews
- Work directly with diverse data types and unparsed parameters
- Port code files cleanly into your external production profiles
Construct Resilient AI Systems
Key structural targets every developer completes before the close of the cohort track.
Baseline Operations
NumPy matrix logic and telemetry pipelines completed.
Model Assembly
Decoder attention layers built from fundamental operations.
Parameter Tuning
SFT paths and parameter alignment arrays run manually.
Graph States
Complex conversational graphs with memory structures configured.
Scale Ops
Deploy tracing layers, serving instances, and validation tests.
Real Engineers. Real Upsides.
See paths taken by practitioners who transitioned into core design assignments.
Aditya N.
GenAI Team Lead
"Before the cohort, I was a backend dev calling generic wrapper endpoints. Assembling semantic retrieval routes and trace evaluations manually altered my profile value completely in review loops."
Priya R.
ML Platform Architect
"Coding backpropagation and custom multi-head blocks removed the technical intimidation of parsing new research text. The focus on metrics and evaluation loops is incredibly practical."
Karan M.
AI Infrastructure Architect
"I entered as a technical manager wondering how to scale AI features reliably. Phase 5 completely demystified latency parameters, tracing logic, and edge deployments."
Alumni Project Review Threads
Direct commentary from engineering professionals who assembled our comprehensive platform paths.
Direct Engineering Guidance
Arjun
✨ Deep Production Experience — Over a decade configuring scalable language implementations, search layers, and structured predictive routes.
🚀 Open Architecture Advocate — Dedicated to training developers on setting up custom open deployments, semantic indexes, and automated test arrays without dependency limits.
🎯 200+ Developers Guided into technical engineering tracks by emphasizing codebase mechanics, clean pull requests, and native framework development over sandbox tools.
Everything Included, Nothing Upsold.
Real enterprise-grade resources stacked into one 24-week cohort. No upsells, no surprises.
Structural Market Premium Calculator
Review directional performance premiums based on localized system infrastructure specializations.
Estimate your jump
Move the sliders below to see where your GenAI specialization track could land you 12 months from now.
Projected Outlook · 12 Months Post Cohort Track
Frequently Asked Questions
Stop Calling APIs.
Start Shipping AI Frameworks.
16 of 30 seats remain. Cohort track launches March 2027. Registration portals freeze automatically when seat limits register filled.
HR 100% Risk Free Enrolment · Full validation refund guarantee active within 96 hours of registration.